import numpy as np

llf = np.array([-239.75290561974])

nobs = np.array([             202])

k = np.array([               4])

k_exog = np.array([               1])

sigma = np.array([ .79291203639424])

chi2 = np.array([ 35036.682546665])

df_model = np.array([               3])

k_ar = np.array([               1])

k_ma = np.array([               2])

params = np.array([ .99954097483478, 
                   -.69022779461512, 
                   -.20477682541104, 
                    .62870949745886])

cov_params = np.array([ .00007344276568, 
                       -.00016074342677, 
                       -.00018478942445, 
                        8.040251506e-06, 
                       -.00016074342677, 
                        .00094099304774, 
                        .00017233777676, 
                        -.0000145011098, 
                       -.00018478942445, 
                        .00017233777676, 
                        .00103352686916, 
                        .00030686101903, 
                        8.040251506e-06, 
                        -.0000145011098, 
                        .00030686101903, 
                        .00067796985496]).reshape(4,4)

xb = np.array([               0, 
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y = np.array([np.nan, 
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resid = np.array([np.nan, 
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yr = np.array([np.nan, 
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                 .2121440321207, 
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               -.29256621003151, 
                 1.012699007988, 
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                .13657745718956, 
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               -.02880969829857, 
                .61310833692551, 
               -.48195922374725, 
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               -.50302714109421, 
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                .35447460412979, 
                .01399240642786, 
                  .682772397995, 
                  .474898904562, 
               -.23161220550537, 
                .93784213066101, 
               -.49919214844704, 
                3.4479112625122, 
                -2.020316362381, 
                .41167178750038, 
                .57102704048157, 
               -2.3206453323364, 
                .98880618810654, 
                 .8800373673439, 
               -.11700607091188, 
                .47710022330284, 
                1.8731729984283, 
               -.46784225106239, 
                3.1723618507385, 
               -4.2380332946777, 
               -5.2703905105591, 
                .70423555374146, 
                .70803683996201, 
                 .7517546415329])

mse = np.array([ .95459979772568, 
                 .71522510051727, 
                 .63122135400772, 
                  .6314896941185, 
                 .63029319047928, 
                 .63014930486679, 
                 .62988424301147, 
                 .62969470024109, 
                 .62953060865402, 
                  .6293950676918, 
                 .62928181886673, 
                 .62918740510941, 
                 .62910866737366, 
                 .62904292345047, 
                 .62898802757263, 
                  .6289421916008, 
                 .62890386581421, 
                 .62887191772461, 
                 .62884521484375, 
                  .6288229227066, 
                 .62880426645279, 
                  .6287887096405, 
                  .6287756562233, 
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                 .62875574827194, 
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                 .62874180078506, 
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                 .62872833013535, 
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                 .62872266769409, 
                 .62872052192688, 
                 .62871867418289, 
                 .62871718406677, 
                 .62871593236923, 
                 .62871485948563, 
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                 .62871104478836, 
                 .62871074676514, 
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                 .62871015071869, 
                  .6287100315094, 
                 .62870991230011, 
                 .62870985269547, 
                 .62870979309082, 
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                 .62870973348618, 
                 .62870967388153, 
                 .62870967388153, 
                 .62870961427689, 
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stdp = np.array([               0, 
                                0, 
                  .05104803293943, 
                  .06663129478693, 
                  .02164618112147, 
                   .0773858949542, 
                  .02606418170035, 
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                  .08366665989161, 
                  .13979130983353, 
                   .1902572363615, 
                   .1306214183569, 
                  .21803694963455, 
                  .11079790443182, 
                  .17274764180183, 
                   .1937662512064, 
                  .20047917962074, 
                  .24034893512726, 
                  .21783453226089, 
                  .29279819130898, 
                  .26804205775261, 
                  .28678458929062, 
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                  .39895334839821, 
                  .41131839156151, 
                  .36645981669426, 
                  .40991494059563, 
                  .41024547815323, 
                  .32657703757286, 
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                  .34933325648308, 
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                  .35077446699142, 
                  .34701564908028, 
                  .37364318966866, 
                  .40170526504517, 
                  .56070649623871, 
                  .41915491223335, 
                  .73478156328201, 
                  .67748892307281, 
                   .7744625210762, 
                  .77825599908829, 
                  .97586625814438, 
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                  .83281141519547, 
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                  .95130664110184, 
                  1.0939226150513, 
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                  1.1004731655121, 
                  1.3512066602707, 
                  1.4703129529953, 
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                   1.775306224823, 
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                  1.1547852754593, 
                  1.0519831180573, 
                  1.1594845056534, 
                  1.0069926977158, 
                  1.0675266981125, 
                  1.1299223899841, 
                  1.0620901584625, 
                  1.0999356508255, 
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                  1.0026944875717, 
                  1.0428657531738, 
                  1.1120204925537, 
                  1.1684119701385, 
                  1.0258769989014, 
                  1.1342295408249, 
                  1.1183958053589, 
                  .91313683986664, 
                  .91156214475632, 
                  1.0540328025818, 
                  .84359037876129, 
                  .75758427381516, 
                  .96401190757751, 
                  .83226495981216, 
                   .8759680390358, 
                  .98239886760712, 
                  .85917687416077, 
                  1.0634194612503, 
                  .99442666769028, 
                   1.153311252594, 
                  1.2288066148758, 
                  1.0869039297104, 
                   1.281947016716, 
                  1.0067318677902, 
                  1.1028815507889, 
                  .82448446750641, 
                  .78489726781845, 
                  1.1850204467773, 
                  .86753690242767, 
                  1.0692945718765, 
                  1.1030179262161, 
                   .8791960477829, 
                  .86451041698456, 
                  1.0455346107483, 
                   1.085998415947, 
                  1.0172398090363, 
                  1.2250980138779, 
                  1.2316122055054, 
                   1.062157869339, 
                  1.3991860151291, 
                  1.0520887374878, 
                  2.2203133106232, 
                  .88833123445511, 
                  1.4289729595184, 
                  1.5206423997879, 
                  .68520504236221, 
                  1.4659557342529, 
                  1.5350053310394, 
                  1.3178979158401, 
                  1.4888265132904, 
                  1.9698411226273, 
                  1.4406447410583, 
                   2.517040014267, 
                  .55537897348404, 
                 -.20722626149654, 
                  1.0899519920349, 
                   1.164245724678])

icstats = np.array([             202, 
                    np.nan, 
                    -239.75290561974, 
                                   4, 
                     487.50581123949, 
                     500.73888202909])


class Bunch(dict):
    def __init__(self, **kw):
        dict.__init__(self, kw)
        self.__dict__  = self


results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, stdp=stdp, icstats=icstats, )
